Improving Disease Detection and Prevention Using Machine Learning Models

Abstract

Machine learning (ML) models have emerged as powerful tools in the field of medical diagnostics and preventive healthcare. Their ability to process large-scale medical data, identify patterns, and make accurate predictions has revolutionized disease detection and prevention strategies. This article explores the application of ML in enhancing early disease diagnosis, risk prediction, and personalized preventive interventions. Through a review of current methodologies and implementation case studies, we evaluate the effectiveness, challenges, and future directions of ML-based healthcare solutions. The study also presents a comparative analysis of model performance in various disease categories, highlighting the potential of ML to transform global health systems.